Enhancing Epidemic Early Warning Systems with Social Media Data

Early warning systems (EWS) play a crucial role in controlling the spread of infectious diseases. This study focused on the integration of social media data within artificial intelligence (AI)-based EWS for improved disease detection and monitoring. We conducted a scoping review to analyze the use of social media data in conjunction with AI-based EWS, aiming to identify the benefits, challenges, and key considerations for developing such systems. Our review examined various studies that employed machine learning models to analyze social media data for public health surveillance. The findings highlight the potential of social media data in enhancing the accuracy, efficiency, and responsiveness of EWS, enabling earlier detection of disease outbreaks and facilitating timely public health interventions. This review provides valuable insights for practitioners, researchers, and policymakers on the integration of social media data within AI-based EWS, contributing to the design and implementation of effective public health surveillance systems.